Discovering predisposing genes for hereditary breast cancer using deep learning.

Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be identified. Discovering predisposing genes contributing to familial BC is challenging due to their presumed rarity, low penetrance, and complex biological mechanisms. Here, we focused on an analysis of rare missense variants in a cohort of 12 families of Middle Eastern origins characterized by a high incidence of BC cases. We devised a novel, high-throughput, variant analysis pipeline adapted for family studies, which aims to analyze variants at the protein level by employing state-of-the-art machine learning models and three-dimensional protein structural analysis. Using our pipeline, we analyzed 1218 rare missense variants that are shared between affected family members and classified 80 genes as candidate pathogenic. Among these genes, we found significant functional enrichment in peroxisomal and mitochondrial biological pathways which segregated across seven families in the study and covered diverse ethnic groups. We present multiple evidence that peroxisomal and mitochondrial pathways play an important, yet underappreciated, role in both germline BC predisposition and BC survival.

Using multi-scale genomics to associate poorly annotated genes with rare diseases.

BACKGROUND: Next-generation sequencing (NGS) has significantly transformed the landscape of identifying disease-causing genes associated with genetic disorders. However, a substantial portion of sequenced patients remains undiagnosed. This may be attributed not only to the challenges posed by harder-to-detect variants, such as non-coding and structural variations but also to the existence of variants in genes not previously associated with the patient's clinical phenotype. This study introduces EvORanker, an algorithm that integrates unbiased data from 1,028 eukaryotic genomes to link mutated genes to clinical phenotypes. METHODS: EvORanker utilizes clinical data, multi-scale phylogenetic profiling, and other omics data to prioritize disease-associated genes. It was evaluated on solved exomes and simulated genomes, compared with existing methods, and applied to 6260 knockout genes with mouse phenotypes lacking human associations. Additionally, EvORanker was made accessible as a user-friendly web tool. RESULTS: In the analyzed exomic cohort, EvORanker accurately identified the "true" disease gene as the top candidate in 69% of cases and within the top 5 candidates in 95% of cases, consistent with results from the simulated dataset. Notably, EvORanker outperformed existing methods, particularly for poorly annotated genes. In the case of the 6260 knockout genes with mouse phenotypes, EvORanker linked 41% of these genes to observed human disease phenotypes. Furthermore, in two unsolved cases, EvORanker successfully identified DLGAP2 and LPCAT3 as disease candidates for previously uncharacterized genetic syndromes. CONCLUSIONS: We highlight clade-based phylogenetic profiling as a powerful systematic approach for prioritizing potential disease genes. Our study showcases the efficacy of EvORanker in associating poorly annotated genes to disease phenotypes observed in patients. The EvORanker server is freely available at https://ccanavati.shinyapps.io/EvORanker/ .

Differentiation shifts from a reversible to an irreversible heterochromatin state at the DM1 locus.

Epigenetic defects caused by hereditary or de novo mutations are implicated in various human diseases. It remains uncertain whether correcting the underlying mutation can reverse these defects in patient cells. Here we show by the analysis of myotonic dystrophy type 1 (DM1)-related locus that in mutant human embryonic stem cells (hESCs), DNA methylation and H3K9me3 enrichments are completely abolished by repeat excision (CTG2000 expansion), whereas in patient myoblasts (CTG2600 expansion), repeat deletion fails to do so. This distinction between undifferentiated and differentiated cells arises during cell differentiation, and can be reversed by reprogramming of gene-edited myoblasts. We demonstrate that abnormal methylation in DM1 is distinctively maintained in the undifferentiated state by the activity of the de novo DNMTs (DNMT3b in tandem with DNMT3a). Overall, the findings highlight a crucial difference in heterochromatin maintenance between undifferentiated (sequence-dependent) and differentiated (sequence-independent) cells, thus underscoring the role of differentiation as a locking mechanism for repressive epigenetic modifications at the DM1 locus.